Research Journal of Applied Sciences, Engineering and Technology 11(1): 40-47, 2015
DOI: 10.19026/rjaset.11.1673
ISSN: 2040-7459; e-ISSN: 2040-7467
© 2015 Maxwell Scientific Publication Corp.
Submitted: January 8, 2015
Accepted: February 27, 2015
Published: September 05, 2015
Research Article
Investigation of Feature Selection Techniques for Face Recognition Using Feature
Fusion Model
1
N. Vijaya Kumar and 2M.S. Irfan Ahmed
Research and Development Centre, Bharathiar University,
2
Sri Krishna Institutions, Sri Krishna College of Engineering and Technology, Coimbatore, India
1
Abstract: This study investigates various feature selection techniques for face recognition. Biometric based
authentication system protects access to resources and has gained importance, because of their reliable, invariant and
discriminating features. An automated biometric system is based on physiological or behavioral human
characteristics for protected access. Biometric trait such as palmprint, iris, hand, voice, face fingerprint, or signature
is used to authenticate a person's claim. Of the biometrics, face recognition is gaining popularity due to its simple
method of capturing the image using cameras. However the number of features generated is high leading to higher
computation time. Using feature selection technique it is shown that recognition rate improves.
Keywords: Biometrics, Correlation-based Feature Selection (CFS), face recognition, k Nearest Neighbor (kNN),
Mutual Information (MI), Naive Bayes (NB), ORL face database
The term multi modal biometrics denotes fusion of
different information types (Anwar et al., 2009) (e.g.,
fingerprint and face of same person or fingerprints from
two diverse fingers of a person). Multi biometrics
addressed issues related to unimodal like (Ross et al.,
2008):
INTRODUCTION
Biometrics identifies people by measuring their
individual anatomy or physiology (hand geometry/
fingerprint), deeply ingrained skill, behavioural
characteristic (signature) or a combination of the two
(like voice). Biometric authentication technologies like
face, hand, finger, iris and speaker recognition are
available and in use. A biometric system is a pattern
recognition system that acquires an individual’s
biometric data extracting a feature set and comparing it
with a database template (Podio, 2002).
Based on the context, a biometric system operates
in verification or identification modes. In the
verification mode, the system corroborates a person’s
identity by comparing captured biometric data with
own biometric template (s) in the system database. In
the identification mode, the system identifies an
individual by searching the database for all users’
templates for a match. The two types of biometrics are
Unimodal and Multimodal Biometrics.
The unimodal rely on evidence of one information
source for authentication (single fingerprint, face) (Ross
and Jain, 2004; Anwar et al., 2009). Some possible
issues in this type of biometric are:
•
•
•
•
•
•
•
•
Non-universality or insufficient population
coverage (reducing failure to enroll rate that
increase population coverage).
It is increasingly difficult for impostors to spoof
multiple biometric traits of legitimately enrolled
individuals.
Multi biometric systems address noisy data issues
(illness affecting voice; scar affecting fingerprint.
Multi biometric systems offer improvement in
matching accuracy of biometric systems based on
information being combined and fusion methodology
adopted (Teoh et al., 2004). Advances in Information
Technology make Information Security inseparable.
Authentication plays an important role in dealing with
security. Information security is concerned with
integrity, confidentiality and information availability in
all forms. There are many tools and techniques
supporting information security management. But
systems based on biometrics evolved supporting some
information security aspects. Biometrics with reference
to biological sciences was studied and applied for
generations and is now viewed as "biological statistics."
Noise in sensed data
Intra-class variations
Inter-class similarities
Non-universality
Spoof attacks
Corresponding Author: N. Vijaya Kumar, Research and Development Centre, Bharathiar University, Coimbatore, India
This work is licensed under a Creative Commons Attribution 4.0 International License (URL: http://creativecommons.org/licenses/by/4.0/).
40
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
Fig. 1: A generic biometric system
Biometric authentication supports identification,
authentication and non-repudiation in information
security. Biometric authentication is popular in
providing personal identification that is important in
applications like credit card fraud and identity theft
which indicate that this is an issue of major concern.
Authentication confirms something (or someone) as
true, i.e. that claims made by or about a thing is true
(Council, 2007; Bhattacharyya et al., 2009).
A complete biometric system is characterized by
three elements namely.
processing subsystems are split into four tasks: feature
extraction, segmentation, quality control and pattern
matching. Features are extracted from the biometric
image which is used for matching to authenticate.
Segmentation step is required for some biometric like
iris to select only the portion of the biometric image
required for authentication. Quality control checks
whether the image received from the sensor is of good
quality and features can be extracted. In case of
defective image, it requests for a new sample. The
remaining subsystem is storage. There are one/many
forms of storage used, based on the biometric system.
Decision subsystem implements system policy by
directing database search. It determines “matches” or
“non-matches” based on distance or similarity measures
from pattern matcher and makes an “accept/reject”
decision based on system policy (Jain et al., 2004).
Biometrics is classified into physiological
biometrics and behavioural biometrics. Physical
Characteristics such as Face, Fingerprint, Iris,
Palmprint, Retina and behavioural Characteristics like
Signature, Voice, Keystroke, Gait are commonly used
biometrics (Kumar and Ryu, 2008). Different
technologies are used for face recognition. One
approach captures a face image using inexpensive
camera (visible spectrum). This models key features
from the central portion of a facial image extracting
features from captured image (s) that do not change
with time and avoids superficial features like facial
expressions or hair. Major facial recognition benefits
include being non-intrusive, hands-free, ensuring
continuous authentication and acceptance by users. A
face recognition system is a computer vision that
automatically identifies a human face from database
images. Face recognition problem is challenging as it
Enrolment sub-system: Here, data samples are
collected from an enrollee. Mostly devices like scanners
and readers are used for this. This stage is crucial as
mistakes lead to identity misrepresentation.
Template representation: At this biometric operation
stage, data samples obtained during enrolment are
gathered and stored for future reference. Gathering and
Storing is usually done by specific software tools.
Matching process subsystem: Input data is compared
with stored data templates in the system for
identification and verification.
Figure 1 shows a generic biometric authentication
system. It generally has five sub-systems: data
collection, transmission, signal processing, decision and
data storage. Biometric systems begin with
measurement of a behavioural or physiological
characteristic (data collection). Some biometric systems
collect data at one location but store and process it at
another. They need data transmission. When much data
is involved, compression is needed before transmission
or storage to save bandwidth and storage space. Signal41
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
has to account for all possible appearance variation due
to illumination, facial features and occlusions change.
Face recognition is used for two tasks (Latha et al.,
2009):
•
•
sample from a set of original and synthesized virtual
training samples are used. It devised a selected useful
training samples based representation approach to
perform face recognition. Results on five widely used
face databases prove that this approach obtained high
face recognition accuracy, with lower computational
complexity than state-of-the-art approaches.
Developing a face recognition system based on
PCA and Self-Organizing Maps (SOM) unsupervised
learning algorithm was focused on by Anggraini
(2014). Preprocessing contains grey scaling, cropping
and binarization. Selected dataset for research was
Essex database at the University of Essex consisting of
7900 face images from 395 individuals (male and
female).
An up-to-date critical survey of still and video
based face recognition research was provided by Zhao
et al. (2003). There are two underlying motivations to
write this survey paper: the first providing an up-to-date
review of current literature and the next is to offer
insights into studies of machine face recognition. To
ensure a comprehensive survey, it categorized present
recognition techniques and presented detailed
descriptions of representative methods in every
category. Also, relevant topics like psychophysical
studies, system evaluation and illumination issues and
variation pose are covered.
Machine learning methods for recognition based on
face and iris biometrics was dealt with by Oravec
(2014). It presented relevant machine learning methods
with focus on Neural Networks (NN). Some NN theory
aspects like visualization of processes in NN, input data
internal representations as base for new feature
extraction methods and classification and compression
applications were addressed. Machine learning methods
are used for feature extraction and classification and are
applicable to biometric systems.
A multi-modal face recognition algorithm was
presented by Jihua et al. (2013). After Feature
extraction using 2DPCA and dimension reduction from
multiple two-dimensional face images (a positive face
and 2 side faces) of a person, it identified through a new
feature Matrix reformed by part of a feature vector of
every 2 dimensional face images. Result of CAS-PEAL
Face Database revealed that this method had higher
recognition rate than that which used one positive face
under same conditions.
A local approach for face recognition based on
combined feature selection methods like Genetic
Algorithm (GA), mRmR features selection algorithm,
GramdtShmidt algorithm and Naive Bayesian classifier
was proposed by Ouarda et al. (2013) which was
compared with global features based face recognition
systems. This study gives a comparative study based on
Recognition rates and Execution times. The Naive
Bayesian classifier based face recognition system tested
on ORL face database showed 78.75% recognition rate
Verification (one-to-one matching)
Identification (one-to-many matching)
Face images acquisition depends on underlying
applications. For instance, surveillance applications
capture face images by a video camera while image
database investigations require static intensity images
from a standard camera. Face recognition’s advantage
of ubiquity and being universal over other biometrics is
that everyone’s face is displayed readily. Uniqueness,
another biometric characteristic is hard to claim at
current accuracy levels. As face shape, specially when
young, is influenced by genotype, identical twins are
hard to tell apart with this technology.
Geometric feature based approaches are earliest
approaches to face recognition and detection (Lu et al.,
2003). Here, significant facial features are detected and
distances among them and other geometric
characteristics are joined in a feature vector to represent
the face. To recognize a face, test image feature vector
and database image should be obtained. Template based
approaches represent a technique to detect faces
(Nefian and Hayes III, 1999). Unlike geometric feature
based approaches template based approaches use
feature vector representing the face template rather than
significant facial features. Correlation based face
detection methods are based on the computation of
normalized cross correlation coefficient (Kekre et al.,
2011). The first step is determining the location of
significant facial features like eyes, nose or mouth.
Importance of robust facial feature detection for
detection and recognition resulted in development of
various facial feature detection algorithms. Philips
introduced a template based face detection/recognition
system using a matching pursuit filter to get face vector
(Phillips, 1998). Matching pursuit algorithm applied to
image iteratively selects from a basis functions
dictionary the best image decomposition by reducing
the image residue in iterations.
This study investigates the impact of feature fusion
and feature selection for face recognition. Features are
extracted using Gabor for texture feature and DCT
energy coefficients. The features are fused and the best
features selected using Correlation based Feature (CFS)
selection and Mutual Information (MI).
LITERATURE REVIEW
Face recognition accuracy was improved by
reducing uncertainty by Yong et al. (2014), to reduce
face representation uncertainty by synthesizing virtual
training samples. Useful training samples similar to test
42
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
training images. It represented a test sample by training
samples from neighbors and highly relevant classes.
WGSRC sparse solution encodes more structure
information and discriminative information than other
sparse representation methods. Results on five face data
sets revealed that the new method outperformed stateof-the-art sparse representation based classification
methods.
A new face recognition using Extended Curvature
Gabor (ECG) Classifier Bunch described by Hwang
et al. (2014) extended Gabor kernels into ECG kernels
by adding a spatial curvature term to kernel and
adjusting Gaussian width at kernel, leading to many
feature candidates being extracted from one image. An
ECG classifier is implemented by applying LDA to
selected feature vector. To overcome accuracy
limitation of one classifier, an ECG classifier bunch
combining multiple ECG classifiers to fusion scheme is
proposed.
and interesting execution times compared to global
approaches.
Nazari and Moin (2013) proposed new face
recognition based on fusing global and local features.
To extract global and local features, it used Gabor
wavelet filter to apply on entire image and nonoverlapping sub-images with equal size. To reduce new
fused feature vector dimension, PCA is used. In
experiments, it used kNN and multi-class SVM
classifiers and ORL database to get face recognition
rate. Results reveal that the new face recognition
algorithm outperforms conventional methods like
global Gabor face recognition and G-2DFLD feature
fusion face recognition regarding recognition rate.
An effective Multi-Sub region Correlation Filter
Bank (MS-CFB) based feature extraction algorithm for
robust face recognition proposed by Yan et al. (2014)
combined benefits of global and local-based feature
extraction algorithms where multiple correlation filters
corresponding to different face sub regions are designed
jointly to optimize overall correlation outputs. It
reduced MS-CFB computational complexity by
designing a correlation filter bank in spatial domain and
improved generalization capitalizing on unconstrained
form during filter bank design process.
A system that collects face images at large standoff
in both daytime and nighttime and presents an
Augmented Heterogeneous Face Recognition (AHFR)
method for cross-distance (150 m probe vs. 1 m gallery)
and cross-spectral (visible light gallery vs. near-infrared
probe) face matching was reported by Kang et al.
(2014). It recovered high-quality face images from
degraded probe images by proposing a Locally Linear
Embedding (LLE) based image restoration method
based. Restored face images are matched to gallery
using a heterogeneous face matcher. Results showed
that new AHFR approach outperformed state-of-the-art
methods for cross-spectral and cross-distance face
matching.
A new noise modeling framework to improve
representation based classification for robust face
recognition was proposed by Zhang et al. (2015).
Representation
based
classification
evoked
repercussions in the face recognition field. The new
framework iteratively first diminishes representation
noise achieving better representation solution for linear
combination till it converges and then exploits
determined ‘optimal’ representation solution and fusion
method to perform classification. Experiments proved
that the new framework simultaneously improves
representation capability by decreasing representation
noise and improving RCBM classification accuracy.
A sparse representation based classification
proposed by Tang et al. (2014) also suggested a new
classification method called Weighted Group Sparse
Representation Classification (WGSRC) to classify
query images by minimizing weighted mixed-norm (ℓ2,
1-norm), regularized reconstruction error regarding
METHODOLOGY
This study proposed face recognition using feature
fusion. Features are extracted using Gabor for texture
feature and DCT. Features are concatenated. Feature
selection uses CFS, MI. Naive Bayes and k Nearest
Neighbor (kNN) are classifiers. ORL face database is
used for evaluating the recognition system.
ORL face database: AT and T (formerly ORL
database) is a standard face database. AT and T
database has face images of 40 distinct persons with
each having 10 different images, taken at different
times, totaling 400. Each database face image has
112×92 pixels size. Facial expressions have variations
like open/closed eyes, smiling/non-smiling and facial
details like glasses/no glasses. All images were taken
against a dark homogeneous background with subjects
in an up-right, frontal position, with tolerance for a few
side movements. There are also scale variations.
Though database was used in many face recognition
researches, it is clear that many samples or database
size are too small to prove eventual results. A higher
size database is essential to prove the accuracy of face
recognition researches.
Feature extraction:
Discrete Cosine Transform (DCT): Discrete Cosine
Transform (DCT) (a Fourier-related transform), is real
valued, which is implemented using the Discrete
Fourier Transform (Rao et al., 1990). The DCT
computes a truncated Chebyshev series. It expresses the
data in terms of sum of cosine functions. The common
type of DCT used functions on a real sequence xn of
length N to produce coefficients Ck, as follows:
43
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
and,
CFS algorithm is a heuristic to evaluate merit of a
features subset. This algorithm considers individual
features usefulness to predict class label with intercorrelation level among them. The heuristic is based on
the hypothesis: Good feature subsets have features
correlated to class, yet uncorrelated to others. In test
theory, the same principle designs a composite test
(sum/average of individual tests) to predict an external
variable of interest where features are individual tests
measuring traits related to variable of interest (class):
where,
The DCT has strong energy compaction property.
DCT has been effectively used in face recognition
(Hafed and Levine, 2001) instead of Karhunen-Loeve
Transform (KLT) as DCT is computationally less
intensive.
(




x exp −2π i ( u0 ( x − x0 ) + v0 ( y − y0 ) )
k + k ( k − 1) r ff
Mutual Information (MI): Entropy is a random
variables uncertainty measure. Let X be a random
variable with discrete values, its uncertainty are
measured by entropy H (X), defined as (Peng et al.,
2005):
Gabor filters: Gabor filters are very useful in image
processing, for it has optimal localization properties in
both frequency and spatial domain (Daugman, 1985).
The Gabor function is a harmonic oscillator present
within a Gaussian envelope and composed of sinusoidal
plane wave. A 2-D gabor filter over an image (x, y) can
be defined as:
 ( x − x0 )2 ( y − y0 )2
G ( x, y ) = exp  −
−

2σ x2
2σ y2

k rcf
Merit s =
H ( X ) = − ∑ p ( x ) log 2 p ( x )
x∈X
where, p (x) = Pr (X = x) is probability density function
of X. Entropy does not depend on actual values, just the
random variable’s probability distribution. For two
discrete random variables X and Y with their
probability density function p (x; y), joint entropy H (X;
Y) is (Fleuret, 2004):
)
where,
(x0, y0) = Specify location in image
(u0, v0) = Specify modulation that has spatial frequency
= + θ0
= Orientation, θ0 = arctan (v0/u0)
σx, σy = Standard deviations
H ( X ,Y ) = −
∑ p ( x, y ) log 2 p ( x, y )
x∈X , y∈Y
Information shared between two random variables
is mutual information. Given variable X, how much
information one gains about variable Y, which is MI I
(X; Y):
Gabor filters are used as bandpass filters to remove
noise. To apply Gabor filters to an image, the frequency
of the sinusoidal plane wave, the filter orientation and
the standard deviations of the Gaussian envelope are to
be specified.
I ( X ;Y )
Feature fusion: The principle of image fusion using
wavelets is to merge the wavelet decompositions of the
two original images using fusion methods like meanmean, max-min, img1, img2 and mean-max applied to
approximation coefficients and detail coefficients. Due
to the difference in correlation among the features, the
mean-max fusion method is used. The combined feature
vectors reduced and classified using the Naive Bayes
and KNN. Fusion at the feature level is used for
selection and combination of features to eliminate
redundant and irrelevant features.
H (X )− H (X Y )
=
=
H ( Y ) − H (Y X
=
p ( x, y )
p ( x , y ) log 2
p (x) p ( y)
−
∑
x∈ X , y ∈Y
)
According to above equation, the MI I (X; Y) will
be big if two variables X and Y are related. If not, I (X;
Y) = 0 if X and Y are unrelated (Estévez et al., 2009).
Classifiers:
Naive bayes: Naïve Bayes classifiers are Bayes
theorem based statistical classifiers using a probabilistic
approach to predict data class, by matching it to class
with highest posterior probability. The following
algorithms are used in Naïve Bayes:
Feature selection:
Correlation-based Feature Selection (CFS): CFS is a
fast, correlation-based filter algorithm applied to
continuous and discrete problems (Ross et al., 2008).
P(CiV ) =
44
P(VCi ) P(Ci )
P(V )
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
where, V = (v1, ……., vn) is document represented in ndimensional attribute vector and C1, ……, Cm
represents m class. But it is computationally expensive
to compute P (V|Ci) to reduce computation, so naïve
assumption of class conditional independence is made.
Hence:
86
Recognition rate
85
P(VCi ) = ∏ k =1 P( xkCi )
n
B
NN
Fu
sio
n -K
on
-N
Fu
si
B
Ga
bo
r-K
NN
orN
Ga
b
NN
T-K
DC
DC
T-N
B
Fig. 2: Recognition rate when feature selection techniques are
not used
88
Recognition rate
87
86
85
84
83
82
NN
Fu
s io
n-K
B
si o
n-N
Fu
Ga
bo
r-K
NN
NB
bo
rGa
NN
T-K
DC
DC
T-N
B
81
Classifiers
Fig. 3: Recognition rate when mutual information is used
89
Recognition rate
88
87
86
85
84
83
NN
Fu
s io
n-K
B
si o
n-N
Fu
NN
Ga
bo
r-K
bo
r-N
B
Ga
T-K
DC
2
k
NN
82
DC
T-N
B
n =1
81
Classifiers
Similarly probability density function (| ) of
observation x is conditioned to hypothesis can
be approximated. It is assumed that is number of
patterns associated to hypothesis , = 1 . . . , so
that 1 + ⋯ + = .
The example is classified by determining samples
majority of labels for K-Near neighbor. This method is
easy to enforce if an example “x” has k nearest
examples where feature space and many of them have
same label “y”, then “x” belongs to “y”. K-NN depends
on further theorem when considering theory. Decision
course considers small nearest neighbor number. So,
example disproportion problem is solved when this
method is used. Though limited nearest neighbor is
considered by K-NN, it is not a decision boundary.
Hence K-NN is suitable to classify example set of
boundary intercross if it overlaps. Euclidian distance is
calculated as follows. Two vectors xi and xj are given
where xi = ( , , , , , ……. , ) and xj = ( ,
, , , , ……. , ).
The difference between xi and xj is:
∑ ( ( xik − xjk ) )
82
79
k
NV
D ( xi , x j ) =
83
80
k-Nearest Neighbour (kNN): k-NN classifier’s
premise is that vector space model is similar for same
documents. Training documents are indexed and
associated with corresponding label. A test document
when submitted, is treated as query retrieving
documents from training set similar to test document.
Test document class label assigning is based on k-NN
distribution. Class label is refined by adding weights
leading to higher accuracy by tuning. k-NN is simple to
understand and easy to implement:
p ( x) ≅
84
Classifiers
RESULTS AND DISCUSSION
Fig. 4: Recognition rate when correlation-based feature
selection is used
Thirty seven people’s images are used for the
conducting experiments to evaluate the proposed work.
Ten images for each person were used and 37×5 images
were used for training and equal amount for testing.
The images are obtained from ORL database. Figure 2
shows the recognition rate when feature selection
techniques are not used.
The proposed fusion technique with Naïve Bayes
classifier approach improved the recognition rate by
4.56% when compared with DCT-NB method showing
the effectiveness of the fusion technique. Similarly
kNN improved the recognition rate by 3.28% when
45
Res. J. Appl. Sci. Eng. Technol., 11(1): 40-47, 2015
compared with DCT-kNN method. Figure 3 shows the
recognition rate when MI is used for feature selection.
It can be seen that MI improves the recognition rate
compared to Fig. 2. The proposed fusion with Naïve
Bayes approach improved the recognition rate by
3.81% when compared with DCT-NB method. The
proposed fusion with kNN improved the recognition
rate by 3.83% when compared with DCT-kNN method.
Figure 4 shows the recognition rate when CFS is
used for feature selection. The performance is better
than Mutual Information based feature selection though
both are statistical techniques.
The proposed fusion with Naïve Bayes approach
improved the recognition rate by 5.01% when
compared with DCT-NB method. The proposed fusion
with kNN improved the recognition rate by 4.4% when
compared with DCT-kNN method.
Hafed, Z.M. and M.D. Levine, 2001. Face recognition
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Hwang, W., X. Huang, S.Z. Li and J. Kim, 2014. Face
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Kang, D., H. Han, A.K. Jain and S.W. Lee, 2014.
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Lu, J., K.N. Plataniotis and A.N. Venetsanopoulos,
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CONCLUSION
Biometrics has the feature of exclusivity and
unchanged, or acceptably changed, over life time of an
individual that is deemed as one of the best solutions to
control access. In this study, we used fusion for the face
recognition. CFS and MI methods were used for the
feature selection and the classifiers were kNN and
Naïve Bayes. The experiments conducted in three
scenarios like without feature section, with CFS and
with MI for the DCT, Gabor and Fusion with the
classifiers. The results demonstrated that the fusion is
outperformed than the DCT and Gabor methods in all
three scenarios.
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